The Impact Of Trade Finance On International Trade: Does Financial .
Research in Business and Economics JournalThe impact of trade finance on international trade: Doesfinancial development matter?Daniel Perez ListonPrairie View A&M UniversityLawrence McNeilPrairie View A&M UniversityABSTRACTApproximately 80 percent of global trade relies on some version of trade finance.This paper seeks to further the understanding of the relationship between trade flows andthe availability of trade finance, while accounting for the development of the samplecountries’ financial sectors. The model also controlled for additional establishedvariables that significantly influence trade patterns, such as import/export demand andexchange rates. The results indicate that trade finance is a positive correlate with exportand import volumes. However, we also find that trade finance becomes even moreimportant in determining trade volumes when countries have a higher level of financialdevelopment.Keywords: Financial Development, Trade Finance, Exports, ImportsCopyright statement: Authors retain the copyright to the manuscripts published inAABRI journals. Please see the AABRI Copyright Policy athttp://www.aabri.com/copyright.html.The impact of trade, page 1
Research in Business and Economics JournalINTRODUCTIONApproximately 80 percent of global trade relies on some version of trade finance.The financing options may vary between open accounts, interfirm trade credit, or bankintermediated trade finance (Chauffour and Malouche, 2011). During the latest financialrecession, short-term trade finance fell precipitously. However, the decline in tradefinance was slightly more pronounced in countries with less-developed financial sectors.For example, during the first quarter of 2009, international bank lending to non-OECDmember countries fell by 14 percent, compared to 10 percent for OECD membercountries (Korinek et al., 2009).The literature supports the view that trade finance, in addition to other factors, isan important determinant of trade flow patterns (Love and Zicchino, 2006; Levchenko etal., 2009; Thomas, 2009; Korinek et al., 2009; Chor and Manova, 2011). The literature,however, fails to examine the role a country’s level of financial development plays ininfluencing the relation between trade finance and trade flow patterns. As a result, theprimary goal and contribution of this paper is to quantitatively assess the impact of tradefinance on trade flows, while accounting for countries’ level of financial development.Specifically, the relative impact of trade credit on trade flows for less financiallydeveloped versus more financially developed countries was compared. In addition, themodel controls for two other well established variables that are known to significantlyinfluence trade flow patterns – import/export demand and exchange rates.Covering a long span and using relatively recent (from 1990 to 2010) countrylevel panel data on trade volumes, gross domestic product , real exchange rates, tradefinance, and a measure of financial development, trade volume equations using variouspanel data methods (pooled least squares, fixed effects GLS, and random effects GLS)were estimated. Similar to prior studies, the results suggest that trade finance is asignificantly positive correlate with export and import volumes (Love and Zicchino,2006; Levchenko et al., 2009; Thomas, 2009; Korinek et al., 2009; Chor and Manova,2011). However, this study shows that the importance of trade finance in determiningimport and export volumes becomes even more important as a country’s level of financialdevelopment increases. From a policy standpoint, the results underscore the rationale forpolicies that lead to the improvement of financial infrastructures.The remainder of this paper is organized as follows. Section 2 briefly discussesthe prior literature. Section 3 describes the data and methodology employed in theeconometric analysis. Section 4 presents the empirical methods used to examine the data.Section 5 discusses the results and Section 6 concludes.LITERATURE REVIEWThere are few studies that specifically examine the long-term relationshipbetween trade financing and trade flows. Moreover, the majority of these studies preferto concentrate on this relationship in the context of financial or banking crises. Forexample, Ronci’s (2004) results indicate that trade finance is only slightly positivelycorrelated with export and import volumes in the short run. However, he further notesthat in periods of financial turmoil, there exists a significantly larger positive relationshipbetween these two variables.The impact of trade, page 2
Research in Business and Economics JournalThe recent global financial crisis has triggered renewed interest on thedeterminants of trade flow patterns (Korinek et al., 2009; Freund, 2009; Evennett, 2009;Kee et al., 2010; Eaton et al., 2010; Levchenko et al., 2010; Chor and Manova, 2011).Many of these studies, however, focus on how trade finance might have led to the largeobserved decreases in trade flows during the crisis. For example, Korinek et al. (2009)examined (pre- and post-crisis) the effects of both the availability and the costs of shortterm trade finance on imports for 43 different countries. Their findings indicate that bothof these factors played a significant role in decreasing trade. Levchenko et al. (2010)studied imports and exports to the U.S. during the latest recession using disaggregatedquarterly and monthly data and found that trade credit did not significantly contribute tothe reduction in imports. Chor and Manova (2011) examined international trade flows forthe latest global financial crisis using monthly, high frequency data on US imports. Incontrast to Levchenko et al. (2010), Chor and Manova find that credit conditions duringthe financial recession did have a significant impact on import volumes.Overall, the aforementioned literature supports the view that trade finance, inaddition to other factors, is an important determinant of trade flow patterns. Theliterature, however, does not take a long-term view of this relationship and it fails toextensively examine the role a country’s level of financial development plays in therelation between trade finance and trade flow patterns. In this paper, it is argued that acountry’s level of financial development is an important factor that influences how tradefinance impacts trade flows. Some authors argue that financial development is anindicator of the degree of financing constraints faced by firms (Love and Zicchino, 2006).For instance, Love and Zicchino (2006) find that financing constraints are larger for firmsin countries with less developed financial systems. In particular, their findings support theview that it is easier for firms to obtain access to external financing in countries where thefinancial sector is highly developed. Therefore, it is reasonable to assume that the impactof trade finance on trade flow patterns might be different between countries with verydifferent levels of financial development.DATA SOURCES AND MEASUREMENT TECHNIQUEThe empirical analysis is conducted using annual data for each country. The dataare retrieved from various sources: the International Monetary Fund’s (IMF) WorldEconomic Outlook and International Financial Statistics (IFS) databases and the WorldBank’s World Development Indicators. The sample spans from 1990 to 2010, for a totalof 21 annual observations per country.1 The variables included in the export and importvolume equations are real exports (exportst,j), real imports (importst,j), real gross domesticproduct (GDPt,j), export demand (EXDEMt,j), real exchange rates (RERt,j), trade finance(FINt,j), and a dummy variable (DUMMYt,j) that takes the value of 1 if a country isfinancially developed and zero otherwise.2 Several researchers have used these variables1Due to data availability, the sample begins in 1990. Table 1 presents the descriptive statistics for all thevariables included in the study, while Table 2 shows the correlation matrix.2A financial development (FD) index was constructed by combining three standardized measures: foreigndirect investment, market capitalization, and stocks traded; all three measures are scaled by GPD. Theassignment of a “1” or “0” is determined by comparing each country’s FD value to the median of the FDindex.The impact of trade, page 3
Research in Business and Economics Journalas predictors of international trade patterns (Love and Zicchino, 2006; Thomas, 2009;Korinek et al., 2009; Freund, 2009; Evennett, 2009; Kee et al., 2010; Eaton et al., 2010;Levchenko et al., 2010; Chor and Manova, 2011). Export and import volumes aremeasured in constant 2000 U.S. dollars and sourced from the World Bank’s WorldDevelopment Indicators. For the export volume equation, export demand representsmarket share and is computed as the ratio of imports to total exports, specificallyEXDEMt,j Σ(𝑖𝑚𝑝𝑜𝑟𝑡𝑠𝑖,𝑡,𝑗 e 𝑖𝑚𝑝𝑜𝑟𝑡𝑠𝑖,𝑡,𝑗 is considered total imports into country i from countries j at time t.𝐸𝑥𝑝𝑜𝑟𝑡𝑠𝑡,𝑗 represents total exports to all countries for country j at time t.Similar to Thomas (2009), the measure of external trade finance (FINt,j) isconstructed by dividing net portfolio inflows to the jth country by the jth country’s grossdomestic product.3 There is evidence which suggests that a country’s lack of financialdevelopment might be compensated by foreign portfolio flows (Manova, 2008a; Antras etal., 2009; and Manova et al., 2009).The real exchange rate and portfolio flow data are obtained from the IMF’sInternational Financial Statistics. Real exchange rates are used to account for relativeprices. While many papers utilize relative export and import prices as an explanatoryvariable of trade flows, this paper used real exchanges rates due to their convenientability to be implemented in large sample set studies. Numerous empirical studiessupport measuring the direct impact of real exchange rates on trade flows (De Gregorioand Wolf, 1994; Boyd et al., 2001; and Bussière et al., 2009).EMPIRICAL MODELTo examine how financial development and trade finance influence trade flows,econometric models similar to those found in Arize (1996), Asafu-Adjeye (1999), andOzturk and Kalyoncu (2009) were estimated; specifically, panel data models. The exportvolume specification is as follows:log(𝑒𝑥𝑝𝑜𝑟𝑡𝑠𝑡,𝑗 ) 𝛼0 𝛼1 log(𝐸𝑋𝐷𝐸𝑀𝑡,𝑗 ) 𝛼2 𝑅𝐸𝑅𝑡,𝑗 𝛼3 𝐹𝐼𝑁𝑡,𝑗 𝛼4 𝐹𝐼𝑁𝑡,𝑗 𝐷𝑈𝑀𝑀𝑌𝑡,𝑗 𝑢𝑡,𝑗 ,(2)where exportst,j are real exports for the jth country at time t, EXDEMt,j is a proxy forexport demand, RERt,j is the real exchange rate index, FINt,j is the trade finance proxy ,and DUMMYt,j is the binary variable equal to 1 if a country is considered financiallydeveloped and zero otherwise.Imports are modeled as follows:3Net portfolio inflows are downloaded from the International Financial Statistics database. This variable isdefined as external liabilities minus external assets. Negative values are indicative of net portfolio outflows,whereas positive values indicate a capital inflow.The impact of trade, page 4
Research in Business and Economics Journallog(𝑖𝑚𝑝𝑜𝑟𝑡𝑠𝑡,𝑗 ) 𝛼0 𝛼1 log(𝐺𝐷𝑃𝑡,𝑗 ) 𝛼2 𝑅𝐸𝑅𝑡,𝑗(3) 𝛼3 𝐹𝐼𝑁𝑡,𝑗 𝛼4 𝐹𝐼𝑁𝑡,𝑗 𝐷𝑈𝑀𝑀𝑌𝑡,𝑗 𝑣𝑡,𝑗 ,where importst,j are real imports for the jth country at time t and GDPt,j is the real grossdomestic product for the jth country. All other variables are defined as before.Finally, this paper utilizes panel data to test for correlations between tradevolumes and the explanatory variables already discussed. The equations are estimatedusing pooled least squares, cross-section fixed effects (FE) GLS which accounts for thepresence of cross-section heteroskedasticity, and cross-section random effects (RE) GLS.The FE model should be used if one suspects that the error terms may be correlated withthe individual effects among the regressors. If the error terms are assumed to beuncorrelated with the regressors, then the RE model should be selected. To determinewhether the FE or RE estimation methodology is appropriate, the Hausman specificationtest is used. This test will determine whether there is a significant correlation betweenunobserved country-specific random effects and the regressors. If the test finds nocorrelation, then the RE model should be used. However, if correlation is found, then theuse of the RE model is inappropirate, and the FE model should be used. The Hausmantest is a type of Wald chi-squared ( 2 ) test with k-1 degrees of freedom, where k is thenumber of regressors. The selection of the FE or RE model is determined by the value ofthe Hausman test statistic m. If m is larger than the critical 2 , then the null hypothesisthat random effects are uncorrelated with the regressors can be rejected and the FE modelshould be selected.RESULTSTables 3 – 8 contain econometric results based on pooled, fixed effect GLS, andrandom effect GLS testing.4 All the tables contain a base model, which includesregressors that are commonly used to explain export and import volumes. For the exportvolume equations, this includes export demand (EXDEM) and real exchange rates (RER).For imports, it includes real gross domestic product (GDP) and real exchange rates(RER). The base models are augmented by including trade finance (FIN), as well as thedummy variable (DUMMY), which accounts for each country’s level of financialdevelopment. Furthermore, trade finance is interacted with the dummy variable.Tables 3-5 report the results from estimating the export volume equations usingpooled, fixed effects, and random effects methods. The base model generally shows thatexport demand (EXDEM) has a positive and significant influence on exports.Furthermore, the results show that the proxy for relative prices (RER) is inversely relatedto exports. That is, as relative prices go up a country loses exports. Next, the tradefinance proxy (FIN) is added to the base model. Across all three specifications, the trade4All tables are located in the Appendices. Export volumes are shown in Tables 3 – 5 and import volumesare in Tables 6 – 8.The impact of trade, page 5
Research in Business and Economics Journalfinance proxy has a positive and significant impact on exports. For example, Table 4,Model 4.2 shows that the estimated coefficient for trade finance is 2.202, and isstatistically significant at the 1 percent level. This indicates that as countries gain accessto external trade financing, they are able to export more.Finally, the interaction of the trade finance proxy with the financial developmentdummy (FIN*DUMMY) is added to Model 4.2. Across all three specifications, theestimated coefficient on the interaction term is positive and significant. For example,Table 4 Model 4.3 shows that the estimated coefficient for trade finance and financialdevelopment interaction term is 2.197, and is statistically significant at the 1 percentlevel. This indicates that countries with greater financial development tend to benefitmore (have higher exports) from trade finance, than do countries with lower financialdevelopment. In general, export volume estimations show that trade finance and financialdevelopment are important determinants of exports.All the export tables indicate a strong positive correlation between export volumeand export demand. This correlation was expected based on the large amount ofempirical work that establishes strong economic relationships between many of thecountries in the sample as well as between countries with relatively high GDPs.However, strength of significance relationship differences emerged when comparing thepooled vs. fixed GLS tables. The pooled models in Table 3 contained significantlystronger correlations between the export volume and export demand.The impact of real exchange rates on exports was expected to be negative. Anegative RER would suggest that as a nation’s currency depreciated against the dollar, theresult would be higher demand for its exports. This expected relationship wassuccessfully established based on Tables 3 – 5. While the correlation between RER andexports was generally very weak, still the negative direction of the relationship, as well asits statistical significance, was affirmed.The import tables also contained a positive and significant relationship betweenimport volumes and domestic demand, as shown in the base models. Moreover, theimpact of real exchange rates on import volumes was positive, suggesting correctly thatas the currency of domestic nations strengthens, their level of imports tends to increase.The strength of RER on import volumes was weak yet significant, which is similar toRER’s relationship with export volumes.The impact of portfolio flows (FIN) on export and import volume was, in mostcases, positive and significant at the 1- and 5-percent levels, indicating the clear role tradefinance has in determining trade flows, which has been recently re-established byChauffour and Malouche (2011). Furthermore, when interacting FIN with the dummyfinancial development variable, an interesting finding was confirmed. Nations at higherlevels of financial development very consistently had a stronger relationship betweentrade finance and trade volume. For the pooled Model 3.3, the elasticity of exportvolume with respect to trade finance was 0.180. However, when interacted with thefinancial development dummy variable, the relationship strengthened to 2.43 andincreased in level of significance. Similar elasticity improvements were found in thefixed and random effects export tables. On the import side, the pooled, fixed effect, andrandom effect interaction coefficients were relatively smaller than the export interactioncoefficients; however, the import coefficients were still robust and significant. TheseThe impact of trade, page 6
Research in Business and Economics Journalfindings suggest that countries with stronger financial infrastructures are able to betterutilize trade finance dollars to positively impact their trade positions.The results of the Hausman test indicate that the FE model is preferred due to them values (Hausman test statistics) being relatively higher than the critical 2 values. Theresults of the majority of the Hausman tests confirm that differences in the coefficientsare systematic; therefore, the preference is in favor of the FE models, which have morerobust parameter values and R 2 s .CONCLUSIONThe goal of this paper was to assess the relationship between trade volume andtrade finance. Using country-level panel data on trade volumes, gross domestic product,real exchange rates, trade finance, and a measure of financial development, the resultsindicate that trade finance is a significantly positive correlate with export and importvolumes. However, it is found that trade finance becomes even more important indetermining import and export volumes when countries have a higher level of financialdevelopment.The specifications were estimated based on pooled least squares, fixed effectsGLS, and random effects GLS modeling techniques. The Hausman test confirmed thatthe more robust FE model was preferred. From a policy standpoint, the resultsunderscore the rationale for policies that lead to the improvement of financialinfrastructures. Therefore, a logical next step of the analysis should be researching: 1)what specific aspects of financial development have the most impact on trade volumesand 2) if specific sets of countries (for example, developed vs. developing) have sharedcharacteristics of financial development that influence trade volumes.The impact of trade, page 7
Research in Business and Economics JournalAPPENDIXTotal Exports,in Billions 7,5005004003002001000-1005,0002,5000--Export % Change,1990-2010Figure 1: Total Exports and Export Percent Change (Low)Low Financially Developed CountriesFigure 1 illustrates cumulative (1990 – 2010) exports for each country classified as“Low” regarding its level of financial development. This data is represented by the solidline and left Y-axis. The percent change in total exports from 1990 to 2010 for eachcountry is represented by the dotted line and right anyJapanMalaysiaNew rlandUnited KingdomUnited States0--Export % Change,1990-2010Total Exports,in Billions Figure 2: Total Exports and Export Percent Change (High)High Financially Developed CountriesFigure 2 illustrates cumulative (1990 – 2010) exports for each country classified as“High” regarding its level of financial development. This data is represented by the solidline and left Y-axis. The percent change in total exports from 1990 to 2010 for eachcountry is represented by the dotted line and right Y-axis.Figure 3: Total Imports and Import Percent Change (Low)The impact of trade, page 8
Total Imports,in Billions talyHungaryGreeceCyprusCote d'IvoireColombiaBrazilAustria0--Import % Change,1990-2010Research in Business and Economics JournalLow Financially Developed CountriesFigure 3 illustrates cumulative (1990 – 2010) imports for each country classified as“Low” regarding its level of financial development. This data is represented by the solidline and left Y-axis. The percent change in total imports from 1990 to 2010 for eachcountry is represented by the dotted line and right eGermanyJapanMalaysiaNew rlandUnited KingdomUnited States0--Imports % Change,1990-2010Total Imports,in Billions Figure 4: Total Imports and Import Percent Change (High)High Financially Developed CountriesFigure 4 illustrates cumulative (1990 – 2010) imports for each country classified as“High” regarding its level of financial development. This data is represented by the solidline and left Y-axis. The percent change in total imports from 1990 to 2010 for eachcountry is represented by the dotted line and right Y-axis.The impact of trade, page 9
Research in Business and Economics JournalTable 1. Descriptive statistics.MeanMedianMaximumMinimumStd. Dev.Exports 165.00 70.80 1,530.00 2.68 245.00Imports 166.00 65.5 1,980.00 1.85 270.00GDP 771.00 162.00 11,700.00 6.19 .150.04RER100.84100.00195.2537.5114.24This table provides descriptive statistics for the variables used in the investigation whichare the: real exports (exportst,j), real imports (importst,j), real gross domestic product(GDPt,j), export demand (EXDEMt,j), trade finance (FINt,j), and the real exchange rate(RERt,j). The sample spans from 1990 through 2010, for a total of 21 annual observationsper country. All data are in an annual frequency. All dollars are in billions. The realexchange rate and the portfolio flow data used to construct the trade finance proxy areobtained from International Financial Statistics (IFS). Data for exports, imports, grossdomestic product, and export demand all come from the IMF’s World Economic Outlookdatabase as well as the World Bank’s World Development Indicators.The impact of trade, page 10
Research in Business and Economics JournalTable 2. Correlation 0.93)(0.00)-----This table provides the correlation matrix for the variables used in the investigationwhich are: real exports (exportst,j), real imports (importst,j), real gross domestic product(GDPt,j), export demand (EXDEMt,j), trade finance (FINt,j), and the real exchange rate(RERt,j). The sample spans from 1990 through 2010, for a total of 21 observations percountry. All data are in an annual frequency. The real exchange rate and the portfolioflow data used to construct the trade finance proxy are obtained from InternationalFinancial Statistics (IFS). Data for exports, imports, gross domestic product, and exportdemand all come from the IMF’s World Economic Outlook database as well as the WorldBank’s World Development Indicators. The p-values are in parentheses.The impact of trade, page 11
Research in Business and Economics JournalTable 3. Export volume equation, pooled least squaresModel 3.1Model 1)FIN1.151***(0.386)FIN*DUMMYModel 0(0.669)2.250***(0.590)Adj. R-squared0.9440.8980.900This table provides the coefficient estimates for the pooled least squares estimation ofequation (2) in the text. Furthermore, the variance-covariance matrix is calculated usingWhite’s cross-section estimator. The variables used in the investigation are: real exports(exportst,j), real imports (importst,j), real gross domestic product (GDPt,j), export demand(EXDEMt,j), trade finance (FINt,j), and the real exchange rate (RERt,j). The sample spansfrom 1990 through 2010, for a total of 21 observations per country. All data are in anannual frequency. The real exchange rate and the portfolio flow data used to construct thetrade finance proxy are obtained from International Financial Statistics (IFS). Data forexports, imports, gross domestic product, and export demand all come from the IMF’sWorld Economic Outlook database as well as the World Bank’s World DevelopmentIndicators. Standard errors are in parentheses and *, **, *** denotes significance at the10%, 5%, and 1% level, respectively.The impact of trade, page 12
Research in Business and Economics JournalTable 4. Export volume equation, GLS fixed effectsModel 4.1Model 000)FIN2.202***(0.494)FIN*DUMMYModel 1.227***(0.488)2.197***(0.869)0.94915.446***Adj. R-squared0.9440.948Redundant fixed14.881***15.214***effects testThis table provides the coefficient estimates for the GLS estimation of equation (2) in thetext. The variables used in the investigation are: real exports (exportst,j), real imports(importst,j), real gross domestic product (GDPt,j), export demand (EXDEMt,j), tradefinance (FINt,j), and the real exchange rate (RERt,j). The sample spans from 1990 through2010, for a total of 21 observations per country. All data are in an annual frequency. Thereal exchange rate and the portfolio flow data used to construct the trade finance proxyare obtained from International Financial Statistics (IFS). Data for exports, imports, grossdomestic product, and export demand all come from the IMF’s World Economic Outlookdatabase as well as the World Bank’s World Development Indicators. Standard errors arein parentheses and *, **, *** denotes significance at the 10%, 5%, and 1% level,respectively. The last column reports the F-statistic for the likelihood ratio test forredundant fixed effects; the null hypothesis is that of redundant fixed effects.The impact of trade, page 13
Research in Business and Economics JournalTable 5. Export volume equation, GLS random effectsModel 5.1Model 62***0.355DUMMY*FINModel *0.4622.131***0.684Adj. R-squared0.6420.6420.645Hausman test27.482***31.189***29.706***This table provides the coefficient estimates for the GLS estimation of equation (2) in thetext. The equations are estimated using cross-section random effects GLS. Furthermore,the variance-covariance matrix is calculated using White’s cross-section estimator. Thevariables used in the investigation are: real exports (exportst,j), real imports (importst,j),real gross domestic product (GDPt,j), export demand (EXDEMt,j), trade finance (FINt,j),and the real exchange rate (RERt,j). The sample spans from 1990 through 2010, for a totalof 21 observations per country. All data are in an annual frequency. The real exchangerate and the portfolio flow data used to construct the trade finance proxy are obtainedfrom International Financial Statistics (IFS). Data for exports, imports, gross domesticproduct, and export demand all come from the IMF’s World Economic Outlook databaseas well as the World Bank’s World Development Indicators. Standard errors are inparentheses and *, **, *** denotes significance at the 10%, 5%, and 1% level,respectively. The last column reports the chi-squared statistic for the Hausman test forcorrelated random effects.The impact of trade, page 14
Research in Business and Economics JournalTable 6. Import volume equation, pooled least squaresModel 6.1Model 637DUMMY*FINModel 0.8261.1381.019Adj. R-squared0.8520.8580.858This table provides the coefficient estimates for the pooled least squares estimation ofequation (3) in the text. The equations are estimated using pooled least squares.Furthermore, the variance-covariance matrix is calculated using White’s cross-sectionestimator. The variables used in the investigation are: real exports (exportst,j), realimports (importst,j), real gross domestic product (GDPt,j), export demand (EXDEMt,j),trade finance (FINt,j), and the real exchange rate (RERt,j). The sample spans from 1990through 2010, for a total of 21 observations per country. All data are in an annualfrequency. The real exchange rate and the portfolio flow data used to construct the tradefinance proxy are obtained from International Financial Statistics (IFS). Data for exports,imports, gross domestic product, and export demand all come from the IMF’s WorldEconomic Outlook database a
The impact of trade, page 2 INTRODUCTION Approximately 80 percent of global trade relies on some version of trade finance. The financing options may vary between open accounts, interfirm trade credit, or bank-intermediated trade finance (Chauffour and Malouche, 2011). During the latest financial recession, short-term trade finance fell .
May 02, 2018 · D. Program Evaluation ͟The organization has provided a description of the framework for how each program will be evaluated. The framework should include all the elements below: ͟The evaluation methods are cost-effective for the organization ͟Quantitative and qualitative data is being collected (at Basics tier, data collection must have begun)
Silat is a combative art of self-defense and survival rooted from Matay archipelago. It was traced at thé early of Langkasuka Kingdom (2nd century CE) till thé reign of Melaka (Malaysia) Sultanate era (13th century). Silat has now evolved to become part of social culture and tradition with thé appearance of a fine physical and spiritual .
̶The leading indicator of employee engagement is based on the quality of the relationship between employee and supervisor Empower your managers! ̶Help them understand the impact on the organization ̶Share important changes, plan options, tasks, and deadlines ̶Provide key messages and talking points ̶Prepare them to answer employee questions
Dr. Sunita Bharatwal** Dr. Pawan Garga*** Abstract Customer satisfaction is derived from thè functionalities and values, a product or Service can provide. The current study aims to segregate thè dimensions of ordine Service quality and gather insights on its impact on web shopping. The trends of purchases have
On an exceptional basis, Member States may request UNESCO to provide thé candidates with access to thé platform so they can complète thé form by themselves. Thèse requests must be addressed to esd rize unesco. or by 15 A ril 2021 UNESCO will provide thé nomineewith accessto thé platform via their émail address.
Chính Văn.- Còn đức Thế tôn thì tuệ giác cực kỳ trong sạch 8: hiện hành bất nhị 9, đạt đến vô tướng 10, đứng vào chỗ đứng của các đức Thế tôn 11, thể hiện tính bình đẳng của các Ngài, đến chỗ không còn chướng ngại 12, giáo pháp không thể khuynh đảo, tâm thức không bị cản trở, cái được
Food outlets which focused on food quality, Service quality, environment and price factors, are thè valuable factors for food outlets to increase thè satisfaction level of customers and it will create a positive impact through word ofmouth. Keyword : Customer satisfaction, food quality, Service quality, physical environment off ood outlets .
Anatomi Tulang dan Fisiologi Panggul 2.1.1 Tulang Tulang pelvis merupakan komposisi dari tiga buah tulang yakni dua tulang kokse . tulang pria lebih kekar dan kuat, sedangkan kerangka perempuan lebih ditujukan kepada pemenuhan fungsi reproduksi. Pada wanita bentuk thorak bagian bawah lebih besar, panggul berbentuk ginekoid dengan ala iliaka lebih lebar dan cekung, promontorium kurang .
